Overview

Dataset statistics

Number of variables19
Number of observations100000
Missing cells0
Missing cells (%)0.0%
Duplicate rows14055
Duplicate rows (%)14.1%
Total size in memory14.5 MiB
Average record size in memory152.0 B

Variable types

Categorical5
Unsupported1
Numeric13

Alerts

Dataset has 14055 (14.1%) duplicate rowsDuplicates
AR_P_Proto_P_DstIP is highly overall correlated with AR_P_Proto_P_SrcIP and 6 other fieldsHigh correlation
AR_P_Proto_P_SrcIP is highly overall correlated with AR_P_Proto_P_DstIP and 6 other fieldsHigh correlation
Pkts_P_State_P_Protocol_P_DestIP is highly overall correlated with AR_P_Proto_P_DstIP and 8 other fieldsHigh correlation
Pkts_P_State_P_Protocol_P_SrcIP is highly overall correlated with AR_P_Proto_P_DstIP and 8 other fieldsHigh correlation
TnP_PerProto is highly overall correlated with AR_P_Proto_P_DstIP and 8 other fieldsHigh correlation
TnP_Per_Dport is highly overall correlated with AR_P_Proto_P_DstIP and 8 other fieldsHigh correlation
daddr is highly overall correlated with TnP_PerProto and 1 other fieldsHigh correlation
dpkts is highly overall correlated with drate and 2 other fieldsHigh correlation
drate is highly overall correlated with dpkts and 2 other fieldsHigh correlation
dur is highly overall correlated with flgs and 1 other fieldsHigh correlation
flgs is highly overall correlated with Pkts_P_State_P_Protocol_P_DestIP and 8 other fieldsHigh correlation
proto is highly overall correlated with flgs and 3 other fieldsHigh correlation
rate is highly overall correlated with AR_P_Proto_P_DstIP and 9 other fieldsHigh correlation
saddr is highly overall correlated with Pkts_P_State_P_Protocol_P_DestIP and 8 other fieldsHigh correlation
spkts is highly overall correlated with AR_P_Proto_P_DstIP and 8 other fieldsHigh correlation
state is highly overall correlated with protoHigh correlation
flgs is highly imbalanced (59.6%)Imbalance
daddr is highly imbalanced (99.3%)Imbalance
dur is highly skewed (γ1 = 59.71403985)Skewed
spkts is highly skewed (γ1 = 315.6675272)Skewed
dpkts is highly skewed (γ1 = 316.2135728)Skewed
rate is highly skewed (γ1 = 29.18979115)Skewed
drate is highly skewed (γ1 = 175.3822442)Skewed
TnP_PerProto is highly skewed (γ1 = 84.37939862)Skewed
TnP_Per_Dport is highly skewed (γ1 = 223.745705)Skewed
AR_P_Proto_P_SrcIP is highly skewed (γ1 = 29.18444864)Skewed
AR_P_Proto_P_DstIP is highly skewed (γ1 = 29.02647228)Skewed
N_IN_Conn_P_DstIP is highly skewed (γ1 = -28.18664968)Skewed
Pkts_P_State_P_Protocol_P_DestIP is highly skewed (γ1 = 79.2465362)Skewed
Pkts_P_State_P_Protocol_P_SrcIP is highly skewed (γ1 = 36.88359724)Skewed
proto is uniformly distributedUniform
dport is an unsupported type, check if it needs cleaning or further analysisUnsupported
dur has 4327 (4.3%) zerosZeros
dpkts has 76429 (76.4%) zerosZeros
rate has 4327 (4.3%) zerosZeros
drate has 94335 (94.3%) zerosZeros
AR_P_Proto_P_SrcIP has 3428 (3.4%) zerosZeros
AR_P_Proto_P_DstIP has 2334 (2.3%) zerosZeros

Reproduction

Analysis started2024-04-14 09:27:41.729109
Analysis finished2024-04-14 09:29:10.593265
Duration1 minute and 28.86 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

flgs
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
e
59219 
e s
39802 
e g
 
928
e *
 
42
eU
 
8

Length

Max length3
Median length1
Mean length1.81554
Min length1

Characters and Unicode

Total characters181554
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowe
2nd rowe
3rd rowe
4th rowe s
5th rowe s

Common Values

ValueCountFrequency (%)
e 59219
59.2%
e s 39802
39.8%
e g 928
 
0.9%
e * 42
 
< 0.1%
eU 8
 
< 0.1%
e & 1
 
< 0.1%

Length

2024-04-14T09:29:10.791715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T09:29:11.104136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
e 99992
71.0%
s 39802
 
28.3%
g 928
 
0.7%
43
 
< 0.1%
eu 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 100000
55.1%
40773
22.5%
s 39802
 
21.9%
g 928
 
0.5%
* 42
 
< 0.1%
U 8
 
< 0.1%
& 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 181554
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 100000
55.1%
40773
22.5%
s 39802
 
21.9%
g 928
 
0.5%
* 42
 
< 0.1%
U 8
 
< 0.1%
& 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 181554
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 100000
55.1%
40773
22.5%
s 39802
 
21.9%
g 928
 
0.5%
* 42
 
< 0.1%
U 8
 
< 0.1%
& 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 181554
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 100000
55.1%
40773
22.5%
s 39802
 
21.9%
g 928
 
0.5%
* 42
 
< 0.1%
U 8
 
< 0.1%
& 1
 
< 0.1%

proto
Categorical

HIGH CORRELATION  UNIFORM 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
tcp
50000 
udp
50000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtcp
2nd rowtcp
3rd rowtcp
4th rowtcp
5th rowtcp

Common Values

ValueCountFrequency (%)
tcp 50000
50.0%
udp 50000
50.0%

Length

2024-04-14T09:29:11.712955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T09:29:11.971231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
tcp 50000
50.0%
udp 50000
50.0%

Most occurring characters

ValueCountFrequency (%)
p 100000
33.3%
t 50000
16.7%
c 50000
16.7%
u 50000
16.7%
d 50000
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 100000
33.3%
t 50000
16.7%
c 50000
16.7%
u 50000
16.7%
d 50000
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 100000
33.3%
t 50000
16.7%
c 50000
16.7%
u 50000
16.7%
d 50000
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 100000
33.3%
t 50000
16.7%
c 50000
16.7%
u 50000
16.7%
d 50000
16.7%

saddr
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
192.168.100.147
26906 
192.168.100.148
25845 
192.168.100.150
23674 
192.168.100.149
23366 
192.168.100.3
 
204
Other values (5)
 
5

Length

Max length25
Median length15
Mean length14.99607
Min length13

Characters and Unicode

Total characters1499607
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row192.168.100.149
2nd row192.168.100.149
3rd row192.168.100.150
4th row192.168.100.148
5th row192.168.100.150

Common Values

ValueCountFrequency (%)
192.168.100.147 26906
26.9%
192.168.100.148 25845
25.8%
192.168.100.150 23674
23.7%
192.168.100.149 23366
23.4%
192.168.100.3 204
 
0.2%
fe80::c0c0:aa20:45b9:bdd9 1
 
< 0.1%
fe80::250:56ff:febe:e9d9 1
 
< 0.1%
192.168.100.27 1
 
< 0.1%
192.168.100.4 1
 
< 0.1%
192.168.100.55 1
 
< 0.1%

Length

2024-04-14T09:29:12.187444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T09:29:12.510351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
192.168.100.147 26906
26.9%
192.168.100.148 25845
25.8%
192.168.100.150 23674
23.7%
192.168.100.149 23366
23.4%
192.168.100.3 204
 
0.2%
fe80::c0c0:aa20:45b9:bdd9 1
 
< 0.1%
fe80::250:56ff:febe:e9d9 1
 
< 0.1%
192.168.100.27 1
 
< 0.1%
192.168.100.4 1
 
< 0.1%
192.168.100.55 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 399785
26.7%
. 299994
20.0%
0 223676
14.9%
8 125845
 
8.4%
9 123368
 
8.2%
2 100001
 
6.7%
6 99999
 
6.7%
4 76119
 
5.1%
7 26907
 
1.8%
5 23679
 
1.6%
Other values (8) 234
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1499607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 399785
26.7%
. 299994
20.0%
0 223676
14.9%
8 125845
 
8.4%
9 123368
 
8.2%
2 100001
 
6.7%
6 99999
 
6.7%
4 76119
 
5.1%
7 26907
 
1.8%
5 23679
 
1.6%
Other values (8) 234
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1499607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 399785
26.7%
. 299994
20.0%
0 223676
14.9%
8 125845
 
8.4%
9 123368
 
8.2%
2 100001
 
6.7%
6 99999
 
6.7%
4 76119
 
5.1%
7 26907
 
1.8%
5 23679
 
1.6%
Other values (8) 234
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1499607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 399785
26.7%
. 299994
20.0%
0 223676
14.9%
8 125845
 
8.4%
9 123368
 
8.2%
2 100001
 
6.7%
6 99999
 
6.7%
4 76119
 
5.1%
7 26907
 
1.8%
5 23679
 
1.6%
Other values (8) 234
 
< 0.1%

daddr
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
192.168.100.3
99784 
192.168.100.150
 
80
192.168.100.147
 
76
192.168.100.149
 
34
192.168.100.148
 
10
Other values (10)
 
16

Length

Max length15
Median length13
Mean length13.00368
Min length7

Characters and Unicode

Total characters1300368
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row192.168.100.3
2nd row192.168.100.3
3rd row192.168.100.3
4th row192.168.100.3
5th row192.168.100.3

Common Values

ValueCountFrequency (%)
192.168.100.3 99784
99.8%
192.168.100.150 80
 
0.1%
192.168.100.147 76
 
0.1%
192.168.100.149 34
 
< 0.1%
192.168.100.148 10
 
< 0.1%
224.0.0.251 3
 
< 0.1%
8.8.8.8 3
 
< 0.1%
192.168.217.2 2
 
< 0.1%
ff02::fb 2
 
< 0.1%
205.251.196.160 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

Length

2024-04-14T09:29:12.853565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
192.168.100.3 99784
99.8%
192.168.100.150 80
 
0.1%
192.168.100.147 76
 
0.1%
192.168.100.149 34
 
< 0.1%
192.168.100.148 10
 
< 0.1%
224.0.0.251 3
 
< 0.1%
8.8.8.8 3
 
< 0.1%
192.168.217.2 2
 
< 0.1%
ff02::fb 2
 
< 0.1%
205.251.196.160 1
 
< 0.1%
Other values (5) 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 300178
23.1%
. 299994
23.1%
0 200062
15.4%
9 100030
 
7.7%
8 100012
 
7.7%
2 100008
 
7.7%
6 99990
 
7.7%
3 99786
 
7.7%
4 125
 
< 0.1%
5 89
 
< 0.1%
Other values (4) 94
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1300368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 300178
23.1%
. 299994
23.1%
0 200062
15.4%
9 100030
 
7.7%
8 100012
 
7.7%
2 100008
 
7.7%
6 99990
 
7.7%
3 99786
 
7.7%
4 125
 
< 0.1%
5 89
 
< 0.1%
Other values (4) 94
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1300368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 300178
23.1%
. 299994
23.1%
0 200062
15.4%
9 100030
 
7.7%
8 100012
 
7.7%
2 100008
 
7.7%
6 99990
 
7.7%
3 99786
 
7.7%
4 125
 
< 0.1%
5 89
 
< 0.1%
Other values (4) 94
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1300368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 300178
23.1%
. 299994
23.1%
0 200062
15.4%
9 100030
 
7.7%
8 100012
 
7.7%
2 100008
 
7.7%
6 99990
 
7.7%
3 99786
 
7.7%
4 125
 
< 0.1%
5 89
 
< 0.1%
Other values (4) 94
 
< 0.1%

dport
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size781.4 KiB

state
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.4 KiB
INT
49991 
REQ
27639 
RST
22231 
ACC
 
129
CON
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300000
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRST
2nd rowREQ
3rd rowREQ
4th rowREQ
5th rowREQ

Common Values

ValueCountFrequency (%)
INT 49991
50.0%
REQ 27639
27.6%
RST 22231
22.2%
ACC 129
 
0.1%
CON 10
 
< 0.1%

Length

2024-04-14T09:29:13.114304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-14T09:29:13.369970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
int 49991
50.0%
req 27639
27.6%
rst 22231
22.2%
acc 129
 
0.1%
con 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
T 72222
24.1%
N 50001
16.7%
I 49991
16.7%
R 49870
16.6%
E 27639
 
9.2%
Q 27639
 
9.2%
S 22231
 
7.4%
C 268
 
0.1%
A 129
 
< 0.1%
O 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 72222
24.1%
N 50001
16.7%
I 49991
16.7%
R 49870
16.6%
E 27639
 
9.2%
Q 27639
 
9.2%
S 22231
 
7.4%
C 268
 
0.1%
A 129
 
< 0.1%
O 10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 72222
24.1%
N 50001
16.7%
I 49991
16.7%
R 49870
16.6%
E 27639
 
9.2%
Q 27639
 
9.2%
S 22231
 
7.4%
C 268
 
0.1%
A 129
 
< 0.1%
O 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 72222
24.1%
N 50001
16.7%
I 49991
16.7%
R 49870
16.6%
E 27639
 
9.2%
Q 27639
 
9.2%
S 22231
 
7.4%
C 268
 
0.1%
A 129
 
< 0.1%
O 10
 
< 0.1%

dur
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct72209
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.828227
Minimum0
Maximum1940.8593
Zeros4327
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-04-14T09:29:13.643298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2313936
Q112.124544
median13.685223
Q314.547841
95-th percentile39.488353
Maximum1940.8593
Range1940.8593
Interquartile range (IQR)2.423297

Descriptive statistics

Standard deviation10.680226
Coefficient of variation (CV)0.72026319
Kurtosis10577.337
Mean14.828227
Median Absolute Deviation (MAD)1.3387595
Skewness59.71404
Sum1482822.7
Variance114.06723
MonotonicityNot monotonic
2024-04-14T09:29:13.966434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4327
 
4.3%
13.717701 15
 
< 0.1%
11.50604 14
 
< 0.1%
12.923795 12
 
< 0.1%
13.812848 12
 
< 0.1%
13.617759 12
 
< 0.1%
12.224451 12
 
< 0.1%
13.750745 11
 
< 0.1%
12.380751 11
 
< 0.1%
13.862002 11
 
< 0.1%
Other values (72199) 95563
95.6%
ValueCountFrequency (%)
0 4327
4.3%
0.007221 1
 
< 0.1%
0.007235 1
 
< 0.1%
0.009813 1
 
< 0.1%
0.057087 1
 
< 0.1%
0.137353 1
 
< 0.1%
0.140991 1
 
< 0.1%
0.141003 1
 
< 0.1%
0.141035 1
 
< 0.1%
0.141037 1
 
< 0.1%
ValueCountFrequency (%)
1940.859253 1
< 0.1%
54.441578 1
< 0.1%
54.440483 1
< 0.1%
54.439243 1
< 0.1%
54.426601 1
< 0.1%
54.420261 1
< 0.1%
54.412254 1
< 0.1%
53.945564 1
< 0.1%
53.386646 1
< 0.1%
49.884693 1
< 0.1%

spkts
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.41457
Minimum1
Maximum35028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-04-14T09:29:14.265728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q39
95-th percentile15
Maximum35028
Range35027
Interquartile range (IQR)4

Descriptive statistics

Standard deviation110.8114
Coefficient of variation (CV)14.945088
Kurtosis99763.838
Mean7.41457
Median Absolute Deviation (MAD)2
Skewness315.66753
Sum741457
Variance12279.167
MonotonicityNot monotonic
2024-04-14T09:29:14.493234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
6 14316
14.3%
5 14019
14.0%
7 12496
12.5%
4 9946
9.9%
8 8204
8.2%
15 7131
7.1%
11 6909
6.9%
3 5895
5.9%
2 4746
 
4.7%
1 4335
 
4.3%
Other values (7) 12003
12.0%
ValueCountFrequency (%)
1 4335
 
4.3%
2 4746
 
4.7%
3 5895
5.9%
4 9946
9.9%
5 14019
14.0%
6 14316
14.3%
7 12496
12.5%
8 8204
8.2%
9 1676
 
1.7%
10 3852
 
3.9%
ValueCountFrequency (%)
35028 1
 
< 0.1%
16 540
 
0.5%
15 7131
7.1%
14 3888
3.9%
13 1846
 
1.8%
12 200
 
0.2%
11 6909
6.9%
10 3852
3.9%
9 1676
 
1.7%
8 8204
8.2%

dpkts
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6528
Minimum0
Maximum35029
Zeros76429
Zeros (%)76.4%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-04-14T09:29:14.720869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum35029
Range35029
Interquartile range (IQR)0

Descriptive statistics

Standard deviation110.77212
Coefficient of variation (CV)169.68769
Kurtosis99994.015
Mean0.6528
Median Absolute Deviation (MAD)0
Skewness316.21357
Sum65280
Variance12270.464
MonotonicityNot monotonic
2024-04-14T09:29:14.928524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 76429
76.4%
1 17906
 
17.9%
2 4757
 
4.8%
3 797
 
0.8%
4 110
 
0.1%
35029 1
 
< 0.1%
ValueCountFrequency (%)
0 76429
76.4%
1 17906
 
17.9%
2 4757
 
4.8%
3 797
 
0.8%
4 110
 
0.1%
35029 1
 
< 0.1%
ValueCountFrequency (%)
35029 1
 
< 0.1%
4 110
 
0.1%
3 797
 
0.8%
2 4757
 
4.8%
1 17906
 
17.9%
0 76429
76.4%

rate
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct32712
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62498175
Minimum0
Maximum138.48499
Zeros4327
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-04-14T09:29:15.215146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.059559
Q10.271586
median0.468331
Q30.656537
95-th percentile1.024237
Maximum138.48499
Range138.48499
Interquartile range (IQR)0.384951

Descriptive statistics

Standard deviation1.3608643
Coefficient of variation (CV)2.1774464
Kurtosis2433.818
Mean0.62498175
Median Absolute Deviation (MAD)0.196377
Skewness29.189791
Sum62498.175
Variance1.8519515
MonotonicityNot monotonic
2024-04-14T09:29:15.498960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4327
 
4.3%
0.484498 47
 
< 0.1%
0.4908 46
 
< 0.1%
0.484437 43
 
< 0.1%
0.484429 42
 
< 0.1%
0.494548 39
 
< 0.1%
0.490686 39
 
< 0.1%
0.484639 39
 
< 0.1%
0.494812 38
 
< 0.1%
1.00271 38
 
< 0.1%
Other values (32702) 95302
95.3%
ValueCountFrequency (%)
0 4327
4.3%
0.048545 4
 
< 0.1%
0.048594 2
 
< 0.1%
0.048595 2
 
< 0.1%
0.048619 2
 
< 0.1%
0.048632 4
 
< 0.1%
0.048664 1
 
< 0.1%
0.048703 3
 
< 0.1%
0.048738 2
 
< 0.1%
0.048789 4
 
< 0.1%
ValueCountFrequency (%)
138.484985 1
< 0.1%
138.216995 1
< 0.1%
101.905632 1
< 0.1%
36.095352 1
< 0.1%
17.517122 1
< 0.1%
14.561021 1
< 0.1%
14.185303 1
< 0.1%
14.184095 1
< 0.1%
14.180877 1
< 0.1%
14.180676 1
< 0.1%

drate
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2865
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0072071357
Minimum0
Maximum18.047676
Zeros94335
Zeros (%)94.3%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-04-14T09:29:15.802439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.042289
Maximum18.047676
Range18.047676
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.071014853
Coefficient of variation (CV)9.8534087
Kurtosis42726.34
Mean0.0072071357
Median Absolute Deviation (MAD)0
Skewness175.38224
Sum720.71357
Variance0.0050431094
MonotonicityNot monotonic
2024-04-14T09:29:16.116991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 94335
94.3%
0.030719 11
 
< 0.1%
0.274514 10
 
< 0.1%
0.046806 9
 
< 0.1%
0.059062 9
 
< 0.1%
0.08579 9
 
< 0.1%
0.06062 9
 
< 0.1%
0.024922 9
 
< 0.1%
0.057298 9
 
< 0.1%
0.042557 8
 
< 0.1%
Other values (2855) 5582
 
5.6%
ValueCountFrequency (%)
0 94335
94.3%
0.021752 1
 
< 0.1%
0.021958 1
 
< 0.1%
0.022284 1
 
< 0.1%
0.02243 6
 
< 0.1%
0.022691 2
 
< 0.1%
0.022765 1
 
< 0.1%
0.023124 4
 
< 0.1%
0.023293 4
 
< 0.1%
0.023314 2
 
< 0.1%
ValueCountFrequency (%)
18.047676 1
< 0.1%
7.236885 1
< 0.1%
0.818594 1
< 0.1%
0.761223 1
< 0.1%
0.738251 1
< 0.1%
0.718613 1
< 0.1%
0.703298 1
< 0.1%
0.640021 1
< 0.1%
0.627743 1
< 0.1%
0.627741 1
< 0.1%

TnP_PerProto
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1369
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean758.37863
Minimum99
Maximum200009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-04-14T09:29:16.431389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile200
Q1500
median690
Q3967
95-th percentile1460
Maximum200009
Range199910
Interquartile range (IQR)467

Descriptive statistics

Standard deviation1820.2793
Coefficient of variation (CV)2.4002249
Kurtosis7888.9053
Mean758.37863
Median Absolute Deviation (MAD)206
Skewness84.379399
Sum75837863
Variance3313416.7
MonotonicityNot monotonic
2024-04-14T09:29:16.777055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
700 6583
 
6.6%
600 4600
 
4.6%
800 3918
 
3.9%
1500 3359
 
3.4%
1100 3125
 
3.1%
500 2669
 
2.7%
100 2319
 
2.3%
400 1997
 
2.0%
1400 1602
 
1.6%
1000 1487
 
1.5%
Other values (1359) 68341
68.3%
ValueCountFrequency (%)
99 10
 
< 0.1%
100 2319
2.3%
102 31
 
< 0.1%
103 11
 
< 0.1%
104 14
 
< 0.1%
106 43
 
< 0.1%
108 57
 
0.1%
110 23
 
< 0.1%
111 7
 
< 0.1%
112 56
 
0.1%
ValueCountFrequency (%)
200009 4
 
< 0.1%
142005 1
 
< 0.1%
124664 3
 
< 0.1%
123687 5
 
< 0.1%
78955 3
 
< 0.1%
1600 211
0.2%
1598 2
 
< 0.1%
1586 2
 
< 0.1%
1585 3
 
< 0.1%
1580 8
 
< 0.1%

TnP_Per_Dport
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1386
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean737.23647
Minimum4
Maximum221903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-04-14T09:29:17.273982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile200
Q1500
median688
Q3966
95-th percentile1460
Maximum221903
Range221899
Interquartile range (IQR)466

Descriptive statistics

Standard deviation784.93658
Coefficient of variation (CV)1.0647012
Kurtosis63029.494
Mean737.23647
Median Absolute Deviation (MAD)208
Skewness223.7457
Sum73723647
Variance616125.44
MonotonicityNot monotonic
2024-04-14T09:29:17.778498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
700 6582
 
6.6%
600 4587
 
4.6%
800 3916
 
3.9%
1500 3359
 
3.4%
1100 3125
 
3.1%
500 2669
 
2.7%
100 2322
 
2.3%
400 1997
 
2.0%
1400 1602
 
1.6%
1000 1487
 
1.5%
Other values (1376) 68354
68.4%
ValueCountFrequency (%)
4 8
 
< 0.1%
5 9
 
< 0.1%
6 40
< 0.1%
7 38
< 0.1%
8 29
< 0.1%
9 54
0.1%
10 13
 
< 0.1%
11 4
 
< 0.1%
12 5
 
< 0.1%
13 1
 
< 0.1%
ValueCountFrequency (%)
221903 1
 
< 0.1%
1600 211
0.2%
1598 2
 
< 0.1%
1586 2
 
< 0.1%
1585 3
 
< 0.1%
1580 8
 
< 0.1%
1576 7
 
< 0.1%
1575 6
 
< 0.1%
1573 4
 
< 0.1%
1572 10
 
< 0.1%

AR_P_Proto_P_SrcIP
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct21626
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0942437
Minimum0
Maximum368.234
Zeros3428
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-04-14T09:29:18.304461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.114418
Q10.359667
median0.54369
Q30.769543
95-th percentile1.0977
Maximum368.234
Range368.234
Interquartile range (IQR)0.409876

Descriptive statistics

Standard deviation5.035319
Coefficient of variation (CV)4.6016431
Kurtosis1559.1907
Mean1.0942437
Median Absolute Deviation (MAD)0.1903935
Skewness29.184449
Sum109424.37
Variance25.354437
MonotonicityNot monotonic
2024-04-14T09:29:18.841465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3428
 
3.4%
1.09834 71
 
0.1%
1.09826 69
 
0.1%
1.07498 67
 
0.1%
1.07497 66
 
0.1%
1.09827 65
 
0.1%
1.09832 52
 
0.1%
0.565167 48
 
< 0.1%
1.09825 46
 
< 0.1%
1.09797 46
 
< 0.1%
Other values (21616) 96042
96.0%
ValueCountFrequency (%)
0 3428
3.4%
0.0728576 5
 
< 0.1%
0.0730214 5
 
< 0.1%
0.0734134 8
 
< 0.1%
0.0734191 1
 
< 0.1%
0.073804 4
 
< 0.1%
0.0738083 5
 
< 0.1%
0.0738423 8
 
< 0.1%
0.0738425 1
 
< 0.1%
0.0741023 3
 
< 0.1%
ValueCountFrequency (%)
368.234 4
< 0.1%
335.94 1
 
< 0.1%
212.911 1
 
< 0.1%
161.638 3
< 0.1%
160.509 2
 
< 0.1%
151.986 3
< 0.1%
139.617 2
 
< 0.1%
121.424 5
< 0.1%
120.35 3
< 0.1%
115.959 4
< 0.1%

AR_P_Proto_P_DstIP
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct17914
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3510358
Minimum0
Maximum409.298
Zeros2334
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-04-14T09:29:19.399544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.132896
Q10.371848
median0.5528165
Q30.759505
95-th percentile1.0926205
Maximum409.298
Range409.298
Interquartile range (IQR)0.387657

Descriptive statistics

Standard deviation8.008706
Coefficient of variation (CV)5.9278268
Kurtosis1194.3308
Mean1.3510358
Median Absolute Deviation (MAD)0.1891355
Skewness29.026472
Sum135103.58
Variance64.139372
MonotonicityNot monotonic
2024-04-14T09:29:19.911295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2334
 
2.3%
1.07498 59
 
0.1%
1.09826 59
 
0.1%
1.09834 58
 
0.1%
1.09827 54
 
0.1%
1.07497 46
 
< 0.1%
1.07496 40
 
< 0.1%
1.07484 35
 
< 0.1%
1.09832 34
 
< 0.1%
1.07495 32
 
< 0.1%
Other values (17904) 97249
97.2%
ValueCountFrequency (%)
0 2334
2.3%
0.0728576 5
 
< 0.1%
0.0738423 8
 
< 0.1%
0.0741164 3
 
< 0.1%
0.074705 6
 
< 0.1%
0.0748962 5
 
< 0.1%
0.0749459 4
 
< 0.1%
0.0749998 7
 
< 0.1%
0.075365 6
 
< 0.1%
0.0756335 4
 
< 0.1%
ValueCountFrequency (%)
409.298 6
< 0.1%
380.33 6
< 0.1%
368.234 4
< 0.1%
345.062 6
< 0.1%
319.366 1
 
< 0.1%
269.721 1
 
< 0.1%
205.118 1
 
< 0.1%
161.638 3
< 0.1%
160.58 4
< 0.1%
159.632 4
< 0.1%

N_IN_Conn_P_DstIP
Real number (ℝ)

SKEWED 

Distinct64
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.89254
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-04-14T09:29:20.356067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile100
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.4956712
Coefficient of variation (CV)0.024983559
Kurtosis868.5507
Mean99.89254
Median Absolute Deviation (MAD)0
Skewness-28.18665
Sum9989254
Variance6.2283746
MonotonicityNot monotonic
2024-04-14T09:29:20.825775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 99609
99.6%
99 114
 
0.1%
79 14
 
< 0.1%
93 14
 
< 0.1%
84 12
 
< 0.1%
69 9
 
< 0.1%
97 9
 
< 0.1%
81 9
 
< 0.1%
96 9
 
< 0.1%
76 9
 
< 0.1%
Other values (54) 192
 
0.2%
ValueCountFrequency (%)
1 3
< 0.1%
2 2
< 0.1%
3 3
< 0.1%
5 1
 
< 0.1%
7 2
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
14 4
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
100 99609
99.6%
99 114
 
0.1%
98 7
 
< 0.1%
97 9
 
< 0.1%
96 9
 
< 0.1%
95 2
 
< 0.1%
93 14
 
< 0.1%
90 3
 
< 0.1%
89 7
 
< 0.1%
86 5
 
< 0.1%

N_IN_Conn_P_SrcIP
Real number (ℝ)

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.80427
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-04-14T09:29:21.190332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30
Q168
median97
Q3100
95-th percentile100
Maximum100
Range99
Interquartile range (IQR)32

Descriptive statistics

Standard deviation24.320148
Coefficient of variation (CV)0.2972968
Kurtosis0.38860823
Mean81.80427
Median Absolute Deviation (MAD)3
Skewness-1.217408
Sum8180427
Variance591.46959
MonotonicityNot monotonic
2024-04-14T09:29:21.496641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 47582
47.6%
68 1126
 
1.1%
96 1016
 
1.0%
98 993
 
1.0%
84 978
 
1.0%
88 961
 
1.0%
99 958
 
1.0%
92 949
 
0.9%
97 932
 
0.9%
93 923
 
0.9%
Other values (90) 43582
43.6%
ValueCountFrequency (%)
1 14
 
< 0.1%
2 29
 
< 0.1%
3 38
 
< 0.1%
4 51
 
0.1%
5 56
0.1%
6 60
0.1%
7 73
0.1%
8 81
0.1%
9 89
0.1%
10 130
0.1%
ValueCountFrequency (%)
100 47582
47.6%
99 958
 
1.0%
98 993
 
1.0%
97 932
 
0.9%
96 1016
 
1.0%
95 914
 
0.9%
94 881
 
0.9%
93 923
 
0.9%
92 949
 
0.9%
91 779
 
0.8%

Pkts_P_State_P_Protocol_P_DestIP
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1504
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean681.7027
Minimum2
Maximum103159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-04-14T09:29:21.796272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile100
Q1370
median640
Q3962
95-th percentile1460
Maximum103159
Range103157
Interquartile range (IQR)592

Descriptive statistics

Standard deviation514.45165
Coefficient of variation (CV)0.75465691
Kurtosis15743.571
Mean681.7027
Median Absolute Deviation (MAD)285
Skewness79.246536
Sum68170270
Variance264660.51
MonotonicityNot monotonic
2024-04-14T09:29:22.104206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
700 6490
 
6.5%
600 4478
 
4.5%
800 3911
 
3.9%
1500 3359
 
3.4%
1100 3125
 
3.1%
500 2580
 
2.6%
100 2360
 
2.4%
400 2043
 
2.0%
1400 1602
 
1.6%
1000 1485
 
1.5%
Other values (1494) 68567
68.6%
ValueCountFrequency (%)
2 5
 
< 0.1%
3 2
 
< 0.1%
4 3
 
< 0.1%
5 6
< 0.1%
6 7
< 0.1%
7 5
 
< 0.1%
8 7
< 0.1%
9 13
< 0.1%
10 12
< 0.1%
11 3
 
< 0.1%
ValueCountFrequency (%)
103159 1
 
< 0.1%
1600 211
0.2%
1598 2
 
< 0.1%
1586 2
 
< 0.1%
1585 3
 
< 0.1%
1580 8
 
< 0.1%
1576 7
 
< 0.1%
1575 6
 
< 0.1%
1573 4
 
< 0.1%
1572 10
 
< 0.1%

Pkts_P_State_P_Protocol_P_SrcIP
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1396
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean577.45915
Minimum1
Maximum70057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.4 KiB
2024-04-14T09:29:22.420461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile80
Q1270
median500
Q3795
95-th percentile1444
Maximum70057
Range70056
Interquartile range (IQR)525

Descriptive statistics

Standard deviation451.89465
Coefficient of variation (CV)0.78255692
Kurtosis5587.3541
Mean577.45915
Median Absolute Deviation (MAD)248
Skewness36.883597
Sum57745915
Variance204208.78
MonotonicityNot monotonic
2024-04-14T09:29:22.715860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
700 4285
 
4.3%
1500 3347
 
3.3%
800 3116
 
3.1%
1100 3087
 
3.1%
600 2831
 
2.8%
500 2021
 
2.0%
400 1717
 
1.7%
1400 1570
 
1.6%
1000 1473
 
1.5%
100 1384
 
1.4%
Other values (1386) 75169
75.2%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 4
 
< 0.1%
3 1
 
< 0.1%
4 9
 
< 0.1%
5 13
< 0.1%
6 22
< 0.1%
7 10
 
< 0.1%
8 25
< 0.1%
9 21
< 0.1%
10 28
< 0.1%
ValueCountFrequency (%)
70057 1
 
< 0.1%
1600 211
0.2%
1598 2
 
< 0.1%
1586 2
 
< 0.1%
1580 8
 
< 0.1%
1573 4
 
< 0.1%
1572 10
 
< 0.1%
1568 7
 
< 0.1%
1552 10
 
< 0.1%
1549 2
 
< 0.1%

Interactions

2024-04-14T09:29:04.877064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:01.282884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:10.936940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:17.382193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:26.500042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:32.107351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:37.279083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:40.936376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:44.622876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:48.698425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:53.138308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:56.908581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:00.471197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:05.273140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:01.840848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:11.603465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:18.853374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:27.081373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:32.578267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:37.564707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:41.214536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:44.895097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:49.114115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:53.415483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:57.201392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:00.744879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:05.688491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:02.402155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:12.085650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:19.661185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:27.635032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:32.991434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:37.843633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:41.491966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:45.166525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:49.514385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:53.683953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:57.483157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:01.018362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:06.096349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:03.220531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:12.647701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:20.609080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:28.065532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:33.341454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:38.125663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:41.764041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:45.449355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:49.837627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:53.940739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:57.769283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:01.298571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:06.494138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:04.474420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:13.267370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:21.736415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:28.583949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:33.695969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:38.434328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:42.024069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:45.735472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:50.208113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:54.226227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:58.045381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:01.585744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:06.828296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:05.319782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:13.612964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:22.592653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:29.198397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:34.116019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:38.711795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:42.293204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:46.013222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:50.584470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:54.496277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:58.329499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:01.861836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:07.121776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:06.791255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:14.043291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:23.071831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:29.685404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:34.569693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:38.998052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:42.819510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:46.310820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:51.025423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:54.780767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:58.612313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:02.263853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:07.400723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:07.657439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:14.408094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:23.491426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:30.061614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:34.919792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:39.287986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:43.055017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:46.580016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:51.408868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:55.040446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:58.853695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:02.679869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:07.706847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:08.107499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:14.920116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:24.111971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:30.512096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:35.351557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:39.574559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:43.329151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:46.853427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:51.803923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:55.613825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:59.126279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:03.055519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:07.970118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:08.479228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:15.346133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:24.607159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:31.036418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:35.761839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:39.831242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:43.595452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:47.105520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:52.099404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:55.854653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:59.402366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:03.475953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:08.233554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:09.085482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:15.910670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:25.026586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:31.296656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:36.128437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:40.110975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:43.841644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:47.448444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:52.349146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:56.117522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:59.665015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:03.824281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:08.516632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:09.739965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:16.475489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:25.461340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:31.557791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:36.466145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:40.388038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:44.103364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:47.835585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:52.590456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:56.379065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:59.916466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:04.196510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:08.792989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:10.261075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:16.893925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:25.906515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:31.822993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:36.855961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:40.658349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:44.351454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:48.254418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:52.851859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:28:56.635151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:00.182394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-14T09:29:04.547834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-04-14T09:29:22.990171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AR_P_Proto_P_DstIPAR_P_Proto_P_SrcIPN_IN_Conn_P_DstIPN_IN_Conn_P_SrcIPPkts_P_State_P_Protocol_P_DestIPPkts_P_State_P_Protocol_P_SrcIPTnP_PerProtoTnP_Per_Dportdaddrdpktsdratedurflgsprotoratesaddrspktsstate
AR_P_Proto_P_DstIP1.0000.9190.0200.0460.7090.5800.6900.6900.408-0.276-0.135-0.1670.0190.0550.8580.0270.6870.227
AR_P_Proto_P_SrcIP0.9191.0000.0200.0850.7300.6360.7050.7050.000-0.216-0.092-0.1340.0160.0470.9320.0110.7380.035
N_IN_Conn_P_DstIP0.0200.0201.0000.0500.0210.009-0.0120.0450.424-0.067-0.073-0.0430.1260.0430.0180.2830.0080.324
N_IN_Conn_P_SrcIP0.0460.0850.0501.0000.1250.4860.0910.0920.037-0.077-0.0470.0440.0390.1010.0830.0340.1110.059
Pkts_P_State_P_Protocol_P_DestIP0.7090.7300.0210.1251.0000.8590.9420.9410.000-0.260-0.0030.2801.0000.0000.7931.0000.9340.316
Pkts_P_State_P_Protocol_P_SrcIP0.5800.6360.0090.4860.8591.0000.8040.8010.000-0.1940.0190.2671.0000.0000.6991.0000.8280.316
TnP_PerProto0.6900.705-0.0120.0910.9420.8041.0000.9940.744-0.2110.0330.3790.5000.0110.7650.6620.9560.378
TnP_Per_Dport0.6900.7050.0450.0920.9410.8010.9941.0000.000-0.2190.0230.3721.0000.0000.7651.0000.9550.316
daddr0.4080.0000.4240.0370.0000.0000.7440.0001.000-0.084-0.096-0.0630.0220.0450.0230.6670.0030.476
dpkts-0.276-0.216-0.067-0.077-0.260-0.194-0.211-0.219-0.0841.0000.5410.1261.0000.000-0.1361.000-0.2680.316
drate-0.135-0.092-0.073-0.047-0.0030.0190.0330.023-0.0960.5411.0000.1550.7070.000-0.0240.7070.0030.316
dur-0.167-0.134-0.0430.0440.2800.2670.3790.372-0.0630.1260.1551.0001.0000.000-0.0291.0000.3400.316
flgs0.0190.0160.1260.0391.0001.0000.5001.0000.0221.0000.7071.0001.0000.830-0.6130.449-0.5580.452
proto0.0550.0470.0430.1010.0000.0000.0110.0000.0450.0000.0000.0000.8301.0000.7120.0510.8091.000
rate0.8580.9320.0180.0830.7930.6990.7650.7650.023-0.136-0.024-0.029-0.6130.7121.0000.5010.8120.323
saddr0.0270.0110.2830.0341.0001.0000.6621.0000.6671.0000.7071.0000.4490.0510.5011.000-0.3110.282
spkts0.6870.7380.0080.1110.9340.8280.9560.9550.003-0.2680.0030.340-0.5580.8090.812-0.3111.0000.316
state0.2270.0350.3240.0590.3160.3160.3780.3160.4760.3160.3160.3160.4521.0000.3230.2820.3161.000

Missing values

2024-04-14T09:29:09.235398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-14T09:29:10.004534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

flgsprotosaddrdaddrdportstatedurspktsdpktsratedrateTnP_PerProtoTnP_Per_DportAR_P_Proto_P_SrcIPAR_P_Proto_P_DstIPN_IN_Conn_P_DstIPN_IN_Conn_P_SrcIPPkts_P_State_P_Protocol_P_DestIPPkts_P_State_P_Protocol_P_SrcIP
0etcp192.168.100.149192.168.100.380RST43.349754620.1614770.0564628668660.1961590.21352710036860300
1etcp192.168.100.149192.168.100.380REQ0.000000100.0000000.0000001001000.0000000.0000001008310083
2etcp192.168.100.150192.168.100.380REQ0.000000100.0000000.0000001001000.0000000.0000001007210072
3e stcp192.168.100.148192.168.100.380REQ12.697832500.3150140.0000004664660.3937700.38902610066466330
4e stcp192.168.100.150192.168.100.380REQ14.341074300.1394600.0000003003000.2092140.209214100100300300
5etcp192.168.100.147192.168.100.380REQ0.000000100.0000000.00000010610640.401200109.808000100339730
6eudp192.168.100.147192.168.100.380INT15.4166751500.9081080.000000117111710.9729720.786549100531171795
7eudp192.168.100.150192.168.100.380INT11.130834600.4492030.0000006006000.5390430.539043100100600600
8e stcp192.168.100.150192.168.100.380REQ11.698979400.2564330.0000004404400.3419260.39647810073320292
9eudp192.168.100.147192.168.100.380INT13.6688711501.0242250.000000149214921.0915301.09153010010014921492
flgsprotosaddrdaddrdportstatedurspktsdpktsratedrateTnP_PerProtoTnP_Per_DportAR_P_Proto_P_SrcIPAR_P_Proto_P_DstIPN_IN_Conn_P_DstIPN_IN_Conn_P_SrcIPPkts_P_State_P_Protocol_P_DestIPPkts_P_State_P_Protocol_P_SrcIP
99990e stcp192.168.100.149192.168.100.380RST10.607552510.4713620.0000008108100.5656340.8340151001081060
99991eudp192.168.100.149192.168.100.380INT10.921830600.4577990.0000006006000.5493580.549358100100600600
99992e stcp192.168.100.147192.168.100.380RST17.094515820.5264850.1452178748740.5976370.53687910066874670
99993eudp192.168.100.149192.168.100.380INT12.384003700.4844960.0000007007000.5652450.56783310065700455
99994eudp192.168.100.147192.168.100.380INT15.5051301400.8384320.000000140014000.9029310.90293110010014001400
99995eudp192.168.100.150192.168.100.380INT12.227951700.4906790.0000007007000.5724590.56981410064700448
99996e stcp192.168.100.148192.168.100.380RST14.710411610.4078740.0000005875870.4800970.40076410061431431
99997eudp192.168.100.150192.168.100.380INT12.451253600.4015660.0000006396390.4817400.51102810087639522
99998e stcp192.168.100.147192.168.100.380REQ13.852234500.2887620.0000005445440.3927200.392720100100390390
99999eudp192.168.100.149192.168.100.380INT12.383963700.4844980.0000007007000.5652470.56929710044700308

Duplicate rows

Most frequently occurring

flgsprotosaddrdaddrstatedurspktsdpktsratedrateTnP_PerProtoTnP_Per_DportAR_P_Proto_P_SrcIPAR_P_Proto_P_DstIPN_IN_Conn_P_DstIPN_IN_Conn_P_SrcIPPkts_P_State_P_Protocol_P_DestIPPkts_P_State_P_Protocol_P_SrcIP# duplicates
795etcp192.168.100.150192.168.100.3REQ0.0100.00.01001000.00.0100100100100546
510etcp192.168.100.149192.168.100.3REQ0.0100.00.01001000.00.0100100100100367
222etcp192.168.100.148192.168.100.3REQ0.0100.00.01001000.00.0100100100100228
25etcp192.168.100.147192.168.100.3REQ0.0100.00.01001000.00.010010010010092
787etcp192.168.100.150192.168.100.3REQ0.0100.00.01001000.00.0100901009018
769etcp192.168.100.150192.168.100.3REQ0.0100.00.01001000.00.0100681006816
505etcp192.168.100.149192.168.100.3REQ0.0100.00.01001000.00.0100951009515
768etcp192.168.100.150192.168.100.3REQ0.0100.00.01001000.00.0100651006515
790etcp192.168.100.150192.168.100.3REQ0.0100.00.01001000.00.0100931009315
204etcp192.168.100.148192.168.100.3REQ0.0100.00.01001000.00.0100691006914